From 372ab305939c9eb58cb46decaf54e9f4e11a6e3d Mon Sep 17 00:00:00 2001 From: Christian Haselgrove Date: Thu, 26 Dec 2024 14:41:39 -0500 Subject: [PATCH 1/6] Update footer --- i18n/en.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/i18n/en.yaml b/i18n/en.yaml index 9bde12c..f7fd10e 100644 --- a/i18n/en.yaml +++ b/i18n/en.yaml @@ -3,9 +3,9 @@ # changeTheme: "Change theme" # copyCode: "Copy code" copyright: | - © 2024 ReproNIM Project + © 2024 ReproNim
- Grant Support: [NIH-NIBIB P41 EB019936](https://projectreporter.nih.gov/project_info_description.cfm?aid=8999833) + Support: [NIH-NIBIB P41 EB019936](https://reporter.nih.gov/project-details/8999833) # dark: "Dark" # editThisPage: "Edit this page on GitHub →" # lastUpdated: "Last updated on" From 28502a33959939cf72c7ffd5c6003867000aba43 Mon Sep 17 00:00:00 2001 From: Christian Haselgrove Date: Thu, 26 Dec 2024 14:43:03 -0500 Subject: [PATCH 2/6] Update outstanding ticket notes --- content/help.md | 2 +- content/resources/_index.md | 2 +- content/resources/estimating-cost.md | 2 +- content/resources/tutorials/_index.md | 2 +- content/resources/tutorials/data-dictionary.md | 2 +- content/resources/tutorials/pond-lake.md | 2 +- content/resources/tutorials/reproschema.md | 2 +- 7 files changed, 7 insertions(+), 7 deletions(-) diff --git a/content/help.md b/content/help.md index 7a70ca9..9ef02f8 100644 --- a/content/help.md +++ b/content/help.md @@ -3,6 +3,6 @@ title: Help toc: false --- -[GH 121](https://github.com/ReproNim/repronim.org/issues/121) +TODO: [GH 121](https://github.com/ReproNim/repronim.org/issues/121) https://docs.google.com/document/d/1-QwhMvTDZVowfO4_H4xrIfc0EeM1Qa9Nmbgjb-nHGsI/edit?pli=1&tab=t.0 diff --git a/content/resources/_index.md b/content/resources/_index.md index 8beebb2..fc06e8c 100644 --- a/content/resources/_index.md +++ b/content/resources/_index.md @@ -3,7 +3,7 @@ title: Resources type: docs --- -[GH 74](https://github.com/ReproNim/repronim.org/issues/74) +TODO: [GH 74](https://github.com/ReproNim/repronim.org/issues/74) This section is dedicated to **how** to make use of the ReproNim ecosystem. Many of the materials here assume you are already familiar with [why neuroimaging should be reproducible](/about/why/). diff --git a/content/resources/estimating-cost.md b/content/resources/estimating-cost.md index 792db14..514ac2f 100644 --- a/content/resources/estimating-cost.md +++ b/content/resources/estimating-cost.md @@ -5,7 +5,7 @@ type: docs weight: 15 --- -[GH 125](https://github.com/ReproNim/repronim.org/issues/125) +TODO: [GH 125](https://github.com/ReproNim/repronim.org/issues/125) ## How much will it cost? diff --git a/content/resources/tutorials/_index.md b/content/resources/tutorials/_index.md index 330b40c..206db7b 100644 --- a/content/resources/tutorials/_index.md +++ b/content/resources/tutorials/_index.md @@ -4,4 +4,4 @@ type: docs weight: 5 --- -[GH 124](https://github.com/ReproNim/repronim.org/issues/124) +TODO: [GH 124](https://github.com/ReproNim/repronim.org/issues/124) diff --git a/content/resources/tutorials/data-dictionary.md b/content/resources/tutorials/data-dictionary.md index 82c7374..5fb0db8 100644 --- a/content/resources/tutorials/data-dictionary.md +++ b/content/resources/tutorials/data-dictionary.md @@ -5,6 +5,6 @@ type: docs weight: 5 --- -[GH 122](https://github.com/ReproNim/repronim.org/issues/122) +TODO: [GH 122](https://github.com/ReproNim/repronim.org/issues/122) https://docs.google.com/document/d/19NE1fRhaBNpu90JJ7nWIjC5WnLimg_l4Sz1FDTxZ-bE/edit?tab=t.0#heading=h.k1s2szmakxpb diff --git a/content/resources/tutorials/pond-lake.md b/content/resources/tutorials/pond-lake.md index 53dbe36..13d0d61 100644 --- a/content/resources/tutorials/pond-lake.md +++ b/content/resources/tutorials/pond-lake.md @@ -5,6 +5,6 @@ type: docs weight: 5 --- -[GH 67](https://github.com/ReproNim/repronim.org/issues/67) +TODO: [GH 67](https://github.com/ReproNim/repronim.org/issues/67) https://docs.google.com/document/d/1wdTsTowf-uzu4LSAvEJ_GDbI9f1WeBSjm2BXcQMAgrI/edit?tab=t.0#heading=h.20whezdqo2bs diff --git a/content/resources/tutorials/reproschema.md b/content/resources/tutorials/reproschema.md index 3dd3dcf..d44fddd 100644 --- a/content/resources/tutorials/reproschema.md +++ b/content/resources/tutorials/reproschema.md @@ -5,6 +5,6 @@ type: docs weight: 5 --- -[GH 95](https://github.com/ReproNim/repronim.org/issues/95) +TODO: [GH 95](https://github.com/ReproNim/repronim.org/issues/95) https://docs.google.com/document/d/1psXPQnrERAKBo7I1OAi1d1ip1ZnOWSYw3YZej0RP_X8/edit?tab=t.0 From 06d582816c62122c7bc907ced1003857f22af53a Mon Sep 17 00:00:00 2001 From: Christian Haselgrove Date: Thu, 26 Dec 2024 14:45:17 -0500 Subject: [PATCH 3/6] Remove colons from headings --- content/resources/tutorials/data-management-and-sharing.md | 6 +++--- content/resources/tutorials/dicom-to-bids.md | 2 +- content/resources/tutorials/repronim-containers.md | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/content/resources/tutorials/data-management-and-sharing.md b/content/resources/tutorials/data-management-and-sharing.md index 7e15d36..b3e1af6 100644 --- a/content/resources/tutorials/data-management-and-sharing.md +++ b/content/resources/tutorials/data-management-and-sharing.md @@ -40,7 +40,7 @@ We also provide some guidance on preparing a DMSP budget for implementing the pl * **Best:** Use NIDM to create or map the data dictionary to standard variable names and value sets. NIDM also provides additional semantics, i.e., the necessary human knowledge for both humans and machines to interpret and relate data elements. NIDM utilizes community ontologies to provide this knowledge in the form of a common vocabulary and relationships between terms, e.g., Freesurfer variable *caudate\_left\_volume* maps to the term “caudate nucleus” in the UBERON anatomical ontology. Caudate nucleus is part of the striatum and telencephalon. * **Metadata for processing pipelines**: Metadata is also important for understanding how the data were processed, e.g., when using Freesurfer to generate a volume of the caudate nucleus, information on the Freesurfer run such as what flags were set should be recorded. NIDM provides a standards-compliant way to capture these details. ReproNim has integrated NIDM into major neuroimaging packages, currently Freesurfer, ANTS, SFL and SPM so that metadata is automatically captured and formatted according to NIDM. -## Element 2: Related Tools, Software and/or Code: +## Element 2: Related Tools, Software and/or Code What tools can be used to work with shared data? @@ -56,7 +56,7 @@ Good software management practices are essential for sharing code and workflows * **Most advanced**: DataLad provides complete versioning of data, code and containerized environments via the container player functionalities, associating all elements of data, code and environment for every operation. -## Element 3: Standards: +## Element 3: Standards As described in the previous elements, the use of standards greatly simplifies managing and sharing data in the broadest possible way. ReproNIM tools are build on a set of community standards for neuroimaging: @@ -100,7 +100,7 @@ Uploading data to a repository can take a while depending on the size and comple Consult with your IRB -## Element 6: Oversight of Data Management and Sharing: +## Element 6: Oversight of Data Management and Sharing Adhering to ReproNim best practices for managing your data and tools ensures that your institutional official can easily assess whether you are in compliance with your DMSP diff --git a/content/resources/tutorials/dicom-to-bids.md b/content/resources/tutorials/dicom-to-bids.md index fcfb807..2fd1043 100644 --- a/content/resources/tutorials/dicom-to-bids.md +++ b/content/resources/tutorials/dicom-to-bids.md @@ -14,7 +14,7 @@ weight: 5 Using standardized file organization and naming schemes form the basis of basic data management, making it easy to understand, work with and find data files. Using *the* standard way of organizing your data that is shared by other members of the field is even better. Why? Because a community standards means that it is much easier to adopt new tools and share your data with your labmates and colleagues. Community standards are integral to the new NIH Data Management and Sharing requirements. Neuroimaging has such a standard: the Brain Imaging Data Structure or BIDS. BIDS has been adopted widely and is the standard required by the OpenNeuro database. Implementing a standard is worthwhile but can be challenging ([see Bush et al., 2022](https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2022.988084/full)). -# Exercise: +# Exercise Here we will learn how to convert data coming off the scanner as **DICOM** files into **nifti** format organized according to **BIDS** using **Heudiconv**, a tool that maps DICOM file information into organized BIDS directories. HeuDiConv **(Heuristic DICOM Converter)** utilizes Python scripts called **heuristics** that provide a set of rules to guide the conversion and organization of DICOM files into BIDS. The use of custom heuristics makes HeuDiConv a versatile and highly configurable tool. diff --git a/content/resources/tutorials/repronim-containers.md b/content/resources/tutorials/repronim-containers.md index ee0f994..e186e70 100644 --- a/content/resources/tutorials/repronim-containers.md +++ b/content/resources/tutorials/repronim-containers.md @@ -27,7 +27,7 @@ Using version control and automation to execute procedures can produce re-execut Following best-practices for file layouts (Datalad + YODA Principles) provide clear connections (via subdatasets) between the source data and the derivative data that is produced. Additionally, utilizing `datalad run` with `repronim-containers` preserves the provenance of exactly what software versions were used and how, leaving a detailed trail for future work. -# Exercise: +# Exercise Let's assume that our goal is to do Quality Control of an MRI dataset (which is available as DataLad dataset ds000003). We will create a new From 0e4b13f256be5e389b410523b9bc51d8c96edf30 Mon Sep 17 00:00:00 2001 From: Christian Haselgrove Date: Thu, 26 Dec 2024 14:50:13 -0500 Subject: [PATCH 4/6] Standardize tutorial headers --- content/resources/tutorials/dicom-to-bids.md | 21 +++++++-- content/resources/tutorials/git.md | 14 +++--- content/resources/tutorials/nipoppy.md | 11 +++-- .../tutorials/repronim-containers.md | 44 ++++++++++++------- 4 files changed, 60 insertions(+), 30 deletions(-) diff --git a/content/resources/tutorials/dicom-to-bids.md b/content/resources/tutorials/dicom-to-bids.md index 2fd1043..9f45854 100644 --- a/content/resources/tutorials/dicom-to-bids.md +++ b/content/resources/tutorials/dicom-to-bids.md @@ -5,10 +5,23 @@ type: docs weight: 5 --- -**ReproPrinciple**: 2a. Data and Metadata management: Use standard data formats and extend them for your needs -**Actions**: Standards, Annotation -**Standards**: BIDS -**Tools**: Heudiconv, ReproIn +**ReproNim principles** + +- 2a. Data and Metadata management: Use standard data formats and extend them for your needs + +**Actions** + +- Standards +- Annotation + +**Standards** + +- BIDS + +**Tools** + +- HeuDiConv +- ReproIn # Challenge diff --git a/content/resources/tutorials/git.md b/content/resources/tutorials/git.md index 7eccafc..1df4b15 100644 --- a/content/resources/tutorials/git.md +++ b/content/resources/tutorials/git.md @@ -5,17 +5,19 @@ type: docs weight: 5 --- -**Repronim principles**: +**ReproNim principles** -[3b: Software management: Use version control from start to finish](https://repronim.netlify.app/about/in-practice/) +- [3b: Software management: Use version control from start to finish](https://repronim.netlify.app/about/in-practice/) -**Actions**: +**Actions** -Implementing version control +- Implementing version control -**Standards**: +**Standards** -**Tools**: [git](https://git-scm.com/) +**Tools** + +- [git](https://git-scm.com/) ## Challenge diff --git a/content/resources/tutorials/nipoppy.md b/content/resources/tutorials/nipoppy.md index 0063090..4d84c4a 100644 --- a/content/resources/tutorials/nipoppy.md +++ b/content/resources/tutorials/nipoppy.md @@ -5,10 +5,13 @@ type: docs weight: 5 --- -**ReproPrinciple**: -**Actions**: -**Standards**: -**Tools**: +**ReproNim principles** + +**Actions** + +**Standards** + +**Tools** # Challenge diff --git a/content/resources/tutorials/repronim-containers.md b/content/resources/tutorials/repronim-containers.md index e186e70..4751385 100644 --- a/content/resources/tutorials/repronim-containers.md +++ b/content/resources/tutorials/repronim-containers.md @@ -4,22 +4,34 @@ type: docs weight: 5 --- -**ReproPrinciples**: - - 2a: Use **standard** data formats and extend them to meet your needs. - - 2b: Use **version control** from start to finish - - 2c: **Annotate** data using standard, reproducible procedures - - 3a: Use released versions of open source software tools. - - 3b: Use **version control** from start to finish - - 3c: Automate the installation of your code and its dependencies - - 3d: Automate the execution of your data analysis - - 3e: **Annotate** your code and workflows using standard, reproducible procedures - - 3f: Use **containers** where reasonable - -**Actions**: Standards, Annotation, Containers, Version Control - -**Standards**: BIDS - -**Tools**: ReproNim Containers, Singularity, Datalad +**ReproNim principles** + +- 2a: Use **standard** data formats and extend them to meet your needs. +- 2b: Use **version control** from start to finish +- 2c: **Annotate** data using standard, reproducible procedures +- 3a: Use released versions of open source software tools. +- 3b: Use **version control** from start to finish +- 3c: Automate the installation of your code and its dependencies +- 3d: Automate the execution of your data analysis +- 3e: **Annotate** your code and workflows using standard, reproducible procedures +- 3f: Use **containers** where reasonable + +**Actions** + +- Standards +- Annotation +- Containers +- Version Control + +**Standards** + +- BIDS + +**Tools** + +- ReproNim Containers +- Singularity +- DataLad # Challenge From 8389620a7e4f8f3c25689c204a816190fbbd6305 Mon Sep 17 00:00:00 2001 From: Christian Haselgrove Date: Thu, 26 Dec 2024 15:00:16 -0500 Subject: [PATCH 5/6] Standardize headers --- content/about/_index.md | 5 ++--- content/about/team.md | 4 ++-- content/about/webinars.md | 4 ++-- content/fellowship/_index.md | 1 - content/resources/getting-started/_index.md | 8 +++---- content/resources/tools/bids/_index.md | 12 +++++----- content/resources/tools/datalad/_index.md | 14 ++++++------ content/resources/tools/heudiconv/_index.md | 14 ++++++------ content/resources/tools/neurobagel/_index.md | 14 ++++++------ content/resources/tools/neurodocker/_index.md | 14 ++++++------ content/resources/tools/nidm/_index.md | 12 +++++----- content/resources/tools/nipoppy/_index.md | 14 ++++++------ content/resources/tools/pynidm/_index.md | 14 ++++++------ content/resources/tools/reproin/_index.md | 14 ++++++------ content/resources/tools/reproschema/_index.md | 14 ++++++------ .../tutorials/data-management-and-sharing.md | 16 +++++++------- content/resources/tutorials/dicom-to-bids.md | 16 +++++++------- content/resources/tutorials/git.md | 10 ++++----- content/resources/tutorials/nipoppy.md | 22 +++++++++---------- .../tutorials/repronim-containers.md | 22 +++++++++---------- 20 files changed, 121 insertions(+), 123 deletions(-) diff --git a/content/about/_index.md b/content/about/_index.md index f0b4caf..81187a3 100644 --- a/content/about/_index.md +++ b/content/about/_index.md @@ -3,9 +3,8 @@ title: About type: docs --- -## ReproNim is a National Center funded through the NIH +ReproNim is a national multi-site technology and research development Center for reproducible neuroimaging computation, funded by a P41 award from the National Institute of Biomedical Imaging and Bioengineering. -ReproNim is a National multi-site technology and research development Center for reproducible neuroimaging computation, funded by a P41 award from the National Institute of Biomedical Imaging and Bioengineering. Collectively, our project and core teams are based across six sites (UMassChan Medical School, MIT, Dartmouth College, McGill, UCSD, and UCI) in North America. ## What we do @@ -31,4 +30,4 @@ The program is open by competitive review, to applicants at all career stages. ## Contact us -Email us at +Email us at . diff --git a/content/about/team.md b/content/about/team.md index eb95d5b..e20252e 100644 --- a/content/about/team.md +++ b/content/about/team.md @@ -34,7 +34,7 @@ weight: 10 - Principal Investigator, NeuroDataScience - [ORIGAMI Research Group](https://neurodatascience.github.io/), Montreal Neurologic Institute - Chair, CTSI, [INCF](https://www.incf.org/team/prof-jean-baptiste-poline) -## Our Current Full Team by Site +## Full team - University of Massachusetts Chan Medical School, Worcester, MA - David Kennedy Principal Investigator @@ -74,6 +74,6 @@ weight: 10 ## Directory -ReproNim Alumni are indicated by asterisks* +ReproNim Alumni are indicated by asterisks. {{< people "repronim-team" >}} diff --git a/content/about/webinars.md b/content/about/webinars.md index 479ac1a..e17b166 100644 --- a/content/about/webinars.md +++ b/content/about/webinars.md @@ -11,13 +11,13 @@ Browse our complete collections of ReproNim Webinar [Videos](https://www.youtube.com/channel/UCGX2sXmEgDuUGWHDSiT1NdQ/videos) and [Slides](https://drive.google.com/drive/folders/1xqgWtghspJtxa8hmC4d6_zUPCgpe4fp-) -# Upcoming Webinars +## Upcoming Webinars ### Friday, January 3, 2025 No Webinar ~ Happy New Year! -# Webinar Presentations to Date +## Webinar Presentations to Date ### Friday, December 6, 2024 at 2pm EST Our featured speaker this month is ReproNim/INCF Fellowship alum [Johanna Bayer](https://nl.linkedin.com/in/johanna-bayer) who joins us from the [Predictive Clinical Neuroscience Group](https://predictiveclinicalneuroscience.com/),(Donders Institute and RadboudUMC, Netherlands). Johanna discusses normative modelling of neuroimaging data using the Predictive Clinical Neuroscience toolkit ([pcntoolkit](https://pcntoolkit.readthedocs.io/en/latest/)), in her presentation: "Normative modelling using the pcntoolkit – the what when and why.” diff --git a/content/fellowship/_index.md b/content/fellowship/_index.md index f2a067f..32c871e 100644 --- a/content/fellowship/_index.md +++ b/content/fellowship/_index.md @@ -5,7 +5,6 @@ title: ReproNim/INCF Fellowship Program type: docs --- - ## Program Overview This is a full year Train-the-Trainer fellowship program which provides Fellows with conceptual and practical training in reproducible neuroimaging, as well as tailored support for individual syllabus development and implementation of reproducibility training in their home institutions. diff --git a/content/resources/getting-started/_index.md b/content/resources/getting-started/_index.md index b7ccfc1..512957a 100644 --- a/content/resources/getting-started/_index.md +++ b/content/resources/getting-started/_index.md @@ -62,7 +62,7 @@ Who would you like to hear from?

-## Sarah +### Sarah Sarah @@ -90,7 +90,7 @@ ReproNIM can help Sarah learn more about data and software management and other * Principle: Publishing re-executable paper * Foundations: Standards, Annotation -## Richard +### Richard Richard @@ -114,7 +114,7 @@ ReproNIM can help Richard learn how standards such as BIDs and NIDM can help wit * Advanced data and software management: Datalad containers/run \+, YODA principles * More use cases are available through our ReproGuide -## John +### John John @@ -136,7 +136,7 @@ ReproNIM provides a [catalog of our main tools](/resources/tools/), with links t → For hands on experience, his students can follow [tutorials](/resources/tutorials/) recommended for Sarah, Richard and Evelyn -## Evelyn +### Evelyn Evelyn diff --git a/content/resources/tools/bids/_index.md b/content/resources/tools/bids/_index.md index 1a9beea..7abc111 100644 --- a/content/resources/tools/bids/_index.md +++ b/content/resources/tools/bids/_index.md @@ -8,21 +8,21 @@ weight: 80 The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging and behavioral data. The standard describes how to organize imaging data (such as NIfTI files), acquisition metadata, subject and session data, and other associated information in structured files and a directory structure. -### Development status +## Development status BIDS is a mature standard widely adopted by the neuroimaging community. Working groups actively maintain and update the standard. -### Innovation +## Innovation BIDS broadens the idea of a data format to standardize the organization of levels of data not usually addressed by traditional formats. BIDS focuses on data organization and not the definition of data elements: by not trying to solve every outstanding problem, BIDS is able to effectively addresses certain issues of data interchange. -### Citation information +## Citation information Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., … Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3(1). https://doi.org/10.1038/sdata.2016.44 [RRID:SCR_016124](https://scicrunch.org/resolver/RRID:SCR_016124) -### How to use +## How to use Ideally the details of BIDS are transparent to the end user. @@ -46,11 +46,11 @@ It is possible to interact with BIDS using: - BIDS Validator - PyBIDS -### Links +## Links - Home page: https://bids.neuroimaging.io/ - Tutorial: https://bids-standard.github.io/bids-starter-kit/ - Full documentation: https://bids.neuroimaging.io/specification.html - How to get help: https://bids.neuroimaging.io/get_involved.html -### Representative publications +## Representative publications diff --git a/content/resources/tools/datalad/_index.md b/content/resources/tools/datalad/_index.md index 46cb927..42378cf 100644 --- a/content/resources/tools/datalad/_index.md +++ b/content/resources/tools/datalad/_index.md @@ -8,33 +8,33 @@ weight: 10 DataLad is a command line tool for data management and sharing. DataLad can download existing DataLad-prepared datasets and can assist in sharing your own data. DataLad can track changes to data and supports data versioning. -### Development status +## Development status DataLad is production software and is actively maintained. -### Innovation +## Innovation By applying source code best practices to data, DataLad has been able to build on existing tools to rapidly build a usable system for data management, versioning, and sharing. -### Citation information +## Citation information Halchenko, Y., Meyer, K., Poldrack, B., Solanky, D., Wagner, A., Gors, J., MacFarlane, D., Pustina, D., Sochat, V., Ghosh, S., Mönch, C., Markiewicz, C., Waite, L., Shlyakhter, I., de la Vega, A., Hayashi, S., Häusler, C., Poline, J.-B., Kadelka, T., … Hanke, M. (2021). DataLad: distributed system for joint management of code, data, and their relationship. Journal of Open Source Software, 6(63), 3262. https://doi.org/10.21105/joss.03262 [RRID:SCR_003931](https://scicrunch.org/resolver/RRID:SCR_003931) -### Requisite knowledge to use +## Requisite knowledge to use - Command line familiarity - Git familiarity is helpful but not mandatory - git-annex familiarity is helpful but not mandatory -### Requisite technical requirements +## Requisite technical requirements - One of the following systems, and proficiency in its installer - Debian (install with apt) - macOS (install with conda or Homebrew) -### Links +## Links - Home page: https://datalad.org - Tutorial: https://handbook.datalad.org/ @@ -43,7 +43,7 @@ Halchenko, Y., Meyer, K., Poldrack, B., Solanky, D., Wagner, A., Gors, J., MacFa - How to get help: https://github.com/datalad/datalad/issues - Testimonials: https://github.com/datalad/datalad/wiki/Testimonials -### Representative publications +## Representative publications Wagner, A. S., Waite, L. K., Wierzba, M., Hoffstaedter, F., Waite, A. Q., Poldrack, B., Eickhoff, S. B., & Hanke, M. (2022). FAIRly big: A framework for computationally reproducible processing of large-scale data. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01163-2 diff --git a/content/resources/tools/heudiconv/_index.md b/content/resources/tools/heudiconv/_index.md index c06f539..5831844 100644 --- a/content/resources/tools/heudiconv/_index.md +++ b/content/resources/tools/heudiconv/_index.md @@ -8,30 +8,30 @@ weight: 20 HeuDiConv (Heuristic DICOM Converter) is a command line converter from DICOM to BIDS (or other structured layouts). HeuDiConv's conversion parameters are encoded as configurable Python code ("heuristics"), enabling flexible and efficient use. -### Development status +## Development status HeuDiConv is production software and is actively maintained. -### Innovation +## Innovation Conversion from DICOM to more usable formats is complicated and requires managing a lot of metadata, but some flexibility is needed to prepare the data in a form appropriate for the next steps. HeuDiConv allows the user to efficiently manage the flexibility while hiding unneeded complexity. -### Citation information +## Citation information [RRID:SCR_017427](https://scicrunch.org/resolver/RRID:SCR_017427) -### Requisite knowledge to use +## Requisite knowledge to use - Command line familiarity - BIDS familiarity - Python proficiency -### Requisite technical requirements +## Requisite technical requirements - A system with Python installed, or - A system with Docker or Singularity installed -### Links +## Links - Home page: https://heudiconv.readthedocs.io/ - Tutorial: https://heudiconv.readthedocs.io/en/latest/tutorials.html @@ -41,4 +41,4 @@ Conversion from DICOM to more usable formats is complicated and requires managin - https://github.com/nipy/heudiconv/issues - https://neurostars.org/tag/heudiconv -### Representative publications +## Representative publications diff --git a/content/resources/tools/neurobagel/_index.md b/content/resources/tools/neurobagel/_index.md index 3fb9d8b..b2d99ce 100644 --- a/content/resources/tools/neurobagel/_index.md +++ b/content/resources/tools/neurobagel/_index.md @@ -10,26 +10,26 @@ Neurobagel is a system for distributed data sharing and discovery. Neurobagel i - Annotating data to prepare it for sharing via Neurobagel. - Adding your data to the Neurobagel network. -### Development status +## Development status Neurobagel is a production system and is actively maintained. -### Innovation +## Innovation Neurobagel is a front end to ReproNim infrastructure technologies. Neurobagel is an immediately-usable tool that uses the standards and technologies that have been developed as technical foundations but until now have needed specialized knowledge to use. -### Citation information +## Citation information -### Requisite knowledge to use +## Requisite knowledge to use - Command line familiarity. - Docker familiarity. -### Requisite technical requirements +## Requisite technical requirements - A system with Docker installed. -### Links +## Links - Home page: https://neurobagel.org/ - Tutorials: @@ -40,4 +40,4 @@ Neurobagel is a front end to ReproNim infrastructure technologies. Neurobagel i - How to get help: https://neurostars.org/tag/neurobagel - Testimonials: https://www.linkedin.com/posts/evavanheese789_you-only-realise-how-important-infrastructure-activity-7220000830463053828-QdDy -### Representative publications +## Representative publications diff --git a/content/resources/tools/neurodocker/_index.md b/content/resources/tools/neurodocker/_index.md index f8a9e9e..b3d201a 100644 --- a/content/resources/tools/neurodocker/_index.md +++ b/content/resources/tools/neurodocker/_index.md @@ -6,32 +6,32 @@ weight: 40 Neurodocker is a command line program for generating Dockerfiles and Singularity recipes for neuroimaging software. -### Development status +## Development status Neurodocker is production software and is actively maintained. -### Innovation +## Innovation While the use of containers is desirable, containerization of tools can be difficult in practice. By abstracting away the details of creating container images, Neurodocker makes containers more accessible and lowers the barrier to their use. -### Citation information +## Citation information [RRID:SCR_017426](https://scicrunch.org/resolver/RRID:SCR_017426) Remi Gau, Jakub Kaczmarzyk, Satrajit Ghosh, Steffen Bollmann, Yaroslav Halchenko, Mathias Goncalves, Matteo Visconti di Oleggio Castello, Dorota Jarecka, Paul Wighton, Michael Notter, Chris Markiewicz, Dylan Nielson, Ghislain Vaillant, araikes, Tom Close, Sooyoung Ahn, Ross Mitchell, Matt Cieslak, Joshua Scarsbrook, … James Kent. (2024). ReproNim/neurodocker: 1.0.1 (1.0.1). Zenodo. https://doi.org/10.5281/zenodo.12675111 -### Requisite knowledge to use +## Requisite knowledge to use - Command line familiarity - Docker or Singularity familiarity -### Requisite technical requirements +## Requisite technical requirements - Linux / macOS - Python 3.8-3.12 - Docker or Singularity -### Links +## Links - Home page: https://www.repronim.org/neurodocker/ - Tutorial: https://miykael.github.io/nipype_tutorial/notebooks/introduction_neurodocker.html @@ -39,4 +39,4 @@ Remi Gau, Jakub Kaczmarzyk, Satrajit Ghosh, Steffen Bollmann, Yaroslav Halchenko - Full documentation: https://www.repronim.org/neurodocker/index.html - How to get help: https://neurostars.org/tag/neurodocker -### Representative publications +## Representative publications diff --git a/content/resources/tools/nidm/_index.md b/content/resources/tools/nidm/_index.md index ac13c78..635b062 100644 --- a/content/resources/tools/nidm/_index.md +++ b/content/resources/tools/nidm/_index.md @@ -6,15 +6,15 @@ weight: 90 The Neuroimaging Data Model (NIDM) is an extension to the W3C PROV standard for human brain imaging. NIDM defines PROV-compatible terms for neuroimaging concepts and relationships. -### Development status +## Development status NIDM is a mature standard widely adopted by the neuroimaging community. Working groups actively maintain and update the standard. -### Innovation +## Innovation By using Semantic Web standards and technologies as its base, NIDM builds on and uses existing frameworks and tools while focusing on neuroimaging-specific concepts. NIDM is developed by community consensus around and open standard rather than as a closed or proprietary definition. -### Citation information +## Citation information Keator, D. B., Helmer, K., Steffener, J., Turner, J. A., Van Erp, T. G. M., Gadde, S., Ashish, N., Burns, G. A., & Nichols, B. N. (2013). Towards structured sharing of raw and derived neuroimaging data across existing resources. NeuroImage, 82, 647–661. https://doi.org/10.1016/j.neuroimage.2013.05.094 @@ -22,7 +22,7 @@ Queder, N., Tien, V. B., Abraham, S. A., Urchs, S. G. W., Helmer, K. G., Chaplin [RRID:SCR_013667](https://scicrunch.org/resolver/RRID:SCR_013667) -### How to use +## How to use Ideally the details of NIDM are invisible to the end user. @@ -42,11 +42,11 @@ It is possible to interact with NIDM using: - PyNIDM - NIDM-Viewer -### Links +## Links - Home page: http://nidm.nidash.org/ - Tutorial: http://nidm.nidash.org/getting-started/ - Full documentation: http://nidm.nidash.org/specs/ - How to get help: https://neurostars.org/tag/nidm -### Representative publications +## Representative publications diff --git a/content/resources/tools/nipoppy/_index.md b/content/resources/tools/nipoppy/_index.md index dbf5d24..a759b2b 100644 --- a/content/resources/tools/nipoppy/_index.md +++ b/content/resources/tools/nipoppy/_index.md @@ -6,26 +6,26 @@ weight: 43 Nipoppy is a command line tool to manage a complete neuroimaging processing workflow. Nipoppy will organize and convert raw data, process it with existing or custom pipelines, and extract derived data for further statistical modeling and analysis. -### Development status +## Development status Nipoppy is complete as a system and is actively maintained. Not all commonly-used pipelines are available out of the box. -### Innovation +## Innovation Nipoppy is the first end-to-end tool for neuroimaging analysis, integrating other ReproNim tools. To achieve this, Nipoppy addressed gaps in the existing tools. By having identified and create ad-hoc solutions to these gaps, Nipoppy has created a road map for further development of existing tools and standards. -### Citation information +## Citation information -### Requisite knowledge to use +## Requisite knowledge to use - Command line familiarity -### Requisite technical requirements +## Requisite technical requirements - Python or conda - Docker -### Links +## Links - Home page: https://nipoppy.readthedocs.io/en/stable/ - Tutorial: https://nipoppy.readthedocs.io/en/stable/quickstart.html @@ -33,4 +33,4 @@ Nipoppy is the first end-to-end tool for neuroimaging analysis, integrating othe - Full documentation: https://nipoppy.readthedocs.io/en/stable/user_guide/index.html - Testimonial: https://www.linkedin.com/posts/evavanheese789_you-only-realise-how-important-infrastructure-activity-7220000830463053828-QdDy -### Representative publications +## Representative publications diff --git a/content/resources/tools/pynidm/_index.md b/content/resources/tools/pynidm/_index.md index d7ddfea..1220e1c 100644 --- a/content/resources/tools/pynidm/_index.md +++ b/content/resources/tools/pynidm/_index.md @@ -17,32 +17,32 @@ PyNIDM is a command line tool for working with [NIDM](../nidm/index.html) data. PyNIDM also provides a utility to query NIDM data using a REST API. -### Development status +## Development status PyNIDM is production software and is actively maintained. -### Innovation +## Innovation PyNIDM is the first tool to allow working with NIDM without needing to understand the details of its semantics. While NIDM itself is a complicated standard that requires precise technical knowledge and some analysis software uses NIDM completely behind the scenes, PyNIDM allows interested users and developers to work with NIDM without being an expert in the standard. -### Citation information +## Citation information [RRID:SCR_021022](https://scicrunch.org/resolver/RRID:SCR_021022) -### Requisite knowledge to use +## Requisite knowledge to use - Command line familiarity - Familiarity with [NIDM](../nidm/index.html) -### Requisite technical requirements +## Requisite technical requirements - Python -### Links +## Links - Home page: https://pynidm.readthedocs.io/ - Installation: https://pynidm.readthedocs.io/en/latest/#installation - Full documentation: https://pynidm.readthedocs.io/ - How to get help: https://github.com/incf-nidash/PyNIDM/issues -### Representative publications +## Representative publications diff --git a/content/resources/tools/reproin/_index.md b/content/resources/tools/reproin/_index.md index 7b1b86f..92f2e1a 100644 --- a/content/resources/tools/reproin/_index.md +++ b/content/resources/tools/reproin/_index.md @@ -8,31 +8,31 @@ ReproIn is a set of best practices for using HeuDiConv and DataLad to prepare ra See also [HeuDiConv](../heudiconv/index.html) and [DataLad](../datalad/index.html). -### Development status +## Development status ReproIn can be used as-is and is actively maintained. -### Innovation +## Innovation ReproIn is a working example of HeuDiConv and DataLad that allows users to use these tools immediately and alter the ReproIn configurations to meet their needs rather than to have to learn HeuDiConv and DataLad from the ground up. -### Citation information +## Citation information [RRID:SCR_017184](https://scicrunch.org/resolver/RRID:SCR_017184) -### Requisite knowledge to use +## Requisite knowledge to use - Working knowledge of HeuDiConv - Working knowledge of DataLad (optional) -### Requisite technical requirements +## Requisite technical requirements - HeuDiconv installed -### Links +## Links - Home page: https://github.com/ReproNim/reproin - Tutorial: https://github.com/ReproNim/reproin#tutorialhowto - Full documentation: https://github.com/ReproNim/reproin -### Representative publications +## Representative publications diff --git a/content/resources/tools/reproschema/_index.md b/content/resources/tools/reproschema/_index.md index dab59ee..2435c8f 100644 --- a/content/resources/tools/reproschema/_index.md +++ b/content/resources/tools/reproschema/_index.md @@ -11,28 +11,28 @@ ReproSchema is a framework for structured survey development and data collection - A server for backend data collection. - A command line tool for preparing new assessments for the ReproSchema. -### Development status +## Development status ReproSchema is complete and is actively maintained. -### Innovation +## Innovation ReproSchema applies reproducible neuroimaging principles to assessments, allowing behavioral and survey data to be included in ReproNim's workflows. ReproSchema provides both the backend infrastructure to do this as well as a front end to immediately use this technology. -### Citation information +## Citation information -### Requisite knowledge to use +## Requisite knowledge to use - Git familiarity - Command line familiarity is helpful - Python familiarity is helpful but not mandatory -### Requisite technical requirements +## Requisite technical requirements - Any platform with Python 3.9+ installed - Package management tools: pip or conda -### Links +## Links - Home page: https://github.com/ReproNim/reproschema - Tutorials: @@ -42,4 +42,4 @@ ReproSchema applies reproducible neuroimaging principles to assessments, allowin - Full documentation: https://www.repronim.org/reproschema/ - How to get help: https://github.com/ReproNim/reproschema/issues -### Representative publications +## Representative publications diff --git a/content/resources/tutorials/data-management-and-sharing.md b/content/resources/tutorials/data-management-and-sharing.md index b3e1af6..9096650 100644 --- a/content/resources/tutorials/data-management-and-sharing.md +++ b/content/resources/tutorials/data-management-and-sharing.md @@ -12,9 +12,9 @@ In some cases, we provide several options, ranked from good/basic to best/advanc Generally the more advanced options require additional effort to implement. We also provide some guidance on preparing a DMSP budget for implementing the plan. -# DMSP Plan elements +## DMSP Plan elements -## Element 1: Data Type +### Element 1: Data Type 1. **Types and amount of scientific data expected to be generated in the project:** * The tools and standards developed/promoted by ReproNIM are appropriate for all types of MRI data including sMRI, fMRI and DTI @@ -40,7 +40,7 @@ We also provide some guidance on preparing a DMSP budget for implementing the pl * **Best:** Use NIDM to create or map the data dictionary to standard variable names and value sets. NIDM also provides additional semantics, i.e., the necessary human knowledge for both humans and machines to interpret and relate data elements. NIDM utilizes community ontologies to provide this knowledge in the form of a common vocabulary and relationships between terms, e.g., Freesurfer variable *caudate\_left\_volume* maps to the term “caudate nucleus” in the UBERON anatomical ontology. Caudate nucleus is part of the striatum and telencephalon. * **Metadata for processing pipelines**: Metadata is also important for understanding how the data were processed, e.g., when using Freesurfer to generate a volume of the caudate nucleus, information on the Freesurfer run such as what flags were set should be recorded. NIDM provides a standards-compliant way to capture these details. ReproNim has integrated NIDM into major neuroimaging packages, currently Freesurfer, ANTS, SFL and SPM so that metadata is automatically captured and formatted according to NIDM. -## Element 2: Related Tools, Software and/or Code +### Element 2: Related Tools, Software and/or Code What tools can be used to work with shared data? @@ -56,7 +56,7 @@ Good software management practices are essential for sharing code and workflows * **Most advanced**: DataLad provides complete versioning of data, code and containerized environments via the container player functionalities, associating all elements of data, code and environment for every operation. -## Element 3: Standards +### Element 3: Standards As described in the previous elements, the use of standards greatly simplifies managing and sharing data in the broadest possible way. ReproNIM tools are build on a set of community standards for neuroimaging: @@ -65,7 +65,7 @@ As described in the previous elements, the use of standards greatly simplifies m * **Common data elements**: The NIH is strongly recommending the use of Common Data Elements to capture data in a standardized way. Many of the common instruments used for behavioral data have CDEs available, e.g., the [Hamilton Depression Scale](https://cde.nlm.nih.gov/cde/search?q=Hamilton%20depression%20scale). ReproNim has created a set of CDEs (aka Federated data elements) that provide a standard for the most common neuroimaging packages. -## Element 4: Data Preservation, Access, and Associated Timelines +### Element 4: Data Preservation, Access, and Associated Timelines 1. **Repository where scientific data and metadata will be archived:** @@ -87,7 +87,7 @@ Uploading data to a repository can take a while depending on the size and comple * **At time of publication or at completion of study as per NIH requirements**: Using the ReproNim framework can expedite sharing dramatically (see above) * Utilizing a trusted repository like OpenNeuro can ensure that data will be available for the long term. -## Element 5: Access, Distribution, or Reuse Considerations +### Element 5: Access, Distribution, or Reuse Considerations * To ensure that clinical data can be shared to the greatest extent possible, utilize [open consent](https://open-brain-consent.readthedocs.io/en/stable/) when designing your study, even if you plan to fully anonymize your data for open sharing. Anonymized data is not considered human data and may be freely shared. However, sharing of clinical data requires authorization by your institutional IRB, and IRBs don’t always agree on what constitutes anonymization. Plus, anonymization is a moving target. If data access control is required, the repository will ensure that data was consented for broad sharing. @@ -100,11 +100,11 @@ Uploading data to a repository can take a while depending on the size and comple Consult with your IRB -## Element 6: Oversight of Data Management and Sharing +### Element 6: Oversight of Data Management and Sharing Adhering to ReproNim best practices for managing your data and tools ensures that your institutional official can easily assess whether you are in compliance with your DMSP -# Preparing a budget for implementing the DMSP +## Preparing a budget for implementing the DMSP The NIH expects that a budget will be provided to cover the costs of implementing your plan. NDA publishes [a general guide](https://nda.nih.gov/nda/data-contribution#cost) on efforts required to prepare data for submission. diff --git a/content/resources/tutorials/dicom-to-bids.md b/content/resources/tutorials/dicom-to-bids.md index 9f45854..cdcb6fd 100644 --- a/content/resources/tutorials/dicom-to-bids.md +++ b/content/resources/tutorials/dicom-to-bids.md @@ -23,18 +23,18 @@ weight: 5 - HeuDiConv - ReproIn -# Challenge +## Challenge Using standardized file organization and naming schemes form the basis of basic data management, making it easy to understand, work with and find data files. Using *the* standard way of organizing your data that is shared by other members of the field is even better. Why? Because a community standards means that it is much easier to adopt new tools and share your data with your labmates and colleagues. Community standards are integral to the new NIH Data Management and Sharing requirements. Neuroimaging has such a standard: the Brain Imaging Data Structure or BIDS. BIDS has been adopted widely and is the standard required by the OpenNeuro database. Implementing a standard is worthwhile but can be challenging ([see Bush et al., 2022](https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2022.988084/full)). -# Exercise +## Exercise Here we will learn how to convert data coming off the scanner as **DICOM** files into **nifti** format organized according to **BIDS** using **Heudiconv**, a tool that maps DICOM file information into organized BIDS directories. HeuDiConv **(Heuristic DICOM Converter)** utilizes Python scripts called **heuristics** that provide a set of rules to guide the conversion and organization of DICOM files into BIDS. The use of custom heuristics makes HeuDiConv a versatile and highly configurable tool. ![image](/images/dicom-bids-inverted.png) [DICOM (left) to BIDS (right)](https://bids.neuroimaging.io/assets/img/dicom-reorganization-transparent-white_1000x477.png) -# Before you start +## Before you start Before diving into the conversion process, it's important to familiarize yourself with the following: @@ -42,14 +42,14 @@ Before diving into the conversion process, it's important to familiarize yoursel * **BIDS (Brain Imaging Data Structure):** A widely adopted standard for organizing neuroimaging data. BIDS enforces a specific directory structure and file naming conventions, promoting data organization, shareability, and compatibility with various analysis tools. We recommend visiting the [BIDS website](https://bids.neuroimaging.io/index.html) and browsing the [BIDS starter kit](https://bids-standard.github.io/bids-starter-kit/) * **Python**: HeuDiConv requires creating a script in Python. -# Step by step guide +## Step by step guide -#### **Step 1: Installing the Necessary Tools** +### Step 1: Installing the Necessary Tools * **HeuDiConv:** Installation instructions can be found on the HeuDiConv GitHub page. You may want to verify the installation instructions on this website are up to date. * **dcm2niix:** HeuDiConv utilizes dcm2niix to convert DICOM files to the NIfTI format, required by BIDS. You'll need to install dcm2niix, separately. You can find installation instructions on the [dcm2niix](https://github.com/rordenlab/dcm2niix) website. -#### **Step 2: Creating a Heuristic File** +### Step 2: Creating a Heuristic File Heuristics are the heart of HeuDiConv's flexibility. They provide the instructions for mapping your specific DICOM data into the desired BIDS structure. You have two options: @@ -91,7 +91,7 @@ Example: Instead of calling your structural imaging protocol “mp\_rage”, ca Implementing these naming conventions means that you do not have to use a custom heuristic file, as HeuDiConv recognizes the ReproIn conventions. -#### **Step 3: Running the Conversion** +### Step 3: Running the Conversion Once you have your heuristic file ready, use the following command to initiate the conversion: @@ -104,7 +104,7 @@ heudiconv \-d DICOM\_DIR\_TEMPLATE \-o BIDS\_OUTDIR \-f HEURISTIC\_FILE \-c dcm2 * **\-b:** Enables BIDS compatibility mode. * **\--minmeta:** Minimizes the amount of metadata written to the output files. You can adjust this based on your requirements. -#### **Step 4: Validation and Next Steps** +### Step 4: Validation and Next Steps After the conversion completes, carefully validate your BIDS output using the BIDS Validator tool. The BIDS Validator helps identify any deviations from the BIDS specification, ensuring your data is properly formatted for future analysis and sharing. You can find the BIDS Validator online. You may want to verify that the website is up to date. diff --git a/content/resources/tutorials/git.md b/content/resources/tutorials/git.md index 1df4b15..094e422 100644 --- a/content/resources/tutorials/git.md +++ b/content/resources/tutorials/git.md @@ -36,7 +36,7 @@ Git not only helps you streamline your workflow but also promotes collaboration, In this exercise we show how you can create your first git repository with your script and how to start collaborating with your colleagues. -# Before you start +## Before you start Before I show you specific steps, I think it is useful to learn more about the concept behind `git` and version control systems in general. I recommend reading: @@ -45,11 +45,11 @@ Before I show you specific steps, I think it is useful to learn more about the c ## Step by step guide -### **Step 1: Installing the Necessary Tools** +### Step 1: Installing the Necessary Tools If you are using Linux or OSX, you will likely already have `git` installed, you can open your terminal and try typing `git –help`. If you need to install on your own, you can follow the instructions from the [website](https://git-scm.com/downloads) (if you are a windows user you might want to install `bash shell` that comes with `git`, you can follow [the instructions](https://carpentries.github.io/workshop-template/install_instructions/#shell)). -### **Step 2: Creating the repository and adding new file** +### Step 2: Creating the repository and adding new file I recommend watching [the ReproNIM/ABCD course lesson, starting from minute 35](https://www.youtube.com/watch?v=SyKmry47SsY&t=2139s&ab_channel=ABCD-ReproNimCourse), if you want to have a bit more guidance (you can start from watching minutes 35-46). @@ -95,7 +95,7 @@ Changes to be committed: new file: fsl\_bet.sh ``` -### **Step 3: Creating a snapshot of the file** +### Step 3: Creating a snapshot of the file You can see, that `git` recognized a new file added to the repository, and reports that there are “changes to be committed”. By running `git commit` command you create a snapshot of the current state of your file in `git`, and that will allow you to track progress and revert to this point (or snapshot) if needed anytime in the future. @@ -116,7 +116,7 @@ nothing to commit, working tree clean So you have confirmation that all changes have been committed (or saved as a snapshot) and the working tree is clean, which means that there are no files that are not tracked by `git`. -### **Step 4: Changing the file content and creating another snapshot** +### Step 4: Changing the file content and creating another snapshot You have a working script, but of course you will want to experiment and change various things. Once you modify the file and run `git status` you will see: diff --git a/content/resources/tutorials/nipoppy.md b/content/resources/tutorials/nipoppy.md index 4d84c4a..11182de 100644 --- a/content/resources/tutorials/nipoppy.md +++ b/content/resources/tutorials/nipoppy.md @@ -13,7 +13,7 @@ weight: 5 **Tools** -# Challenge +## Challenge Managing neuroimaging pipelines often involves handling complex datasets and multiple tools, all while ensuring reproducibility throughout the process. The challenge lies in: @@ -21,7 +21,7 @@ Managing neuroimaging pipelines often involves handling complex datasets and mul * **Workflow management:** Keeping track of different pipeline steps, tools, and configurations can be overwhelming, especially in collaborative projects. * **Reproducibility:** Ensuring that workflows are transparent, repeatable, and compatible with community standards such as BIDS. -# Exercise +## Exercise In this tutorial, you will learn how to use [`nipoppy`](https://nipoppy.readthedocs.io/en/latest/) to address these challenges by: @@ -29,9 +29,9 @@ In this tutorial, you will learn how to use [`nipoppy`](https://nipoppy.readthed 2. **Running the pipeline:** Executing the MRIQC 23.1.0 pipeline our example dataset. 3. **Tracking progress:** Monitoring the pipeline's status and ensuring all steps are completed successfully. -# Step-by-step guide +## Step-by-step guide -## Step 0: Prerequisites +### Step 0: Prerequisites We will utilize the dataset **ds004101** from [OpenNeuro](https://openneuro.org/), which includes structural and functional MRI data for 9 subjects (2 sessions), as our working example. The dataset can be downloaded by following instructions [here](https://openneuro.org/datasets/ds004101/versions/1.0.1/download). @@ -41,7 +41,7 @@ We will utilize the dataset **ds004101** from [OpenNeuro](https://openneuro.org/ - Python environment with Nipoppy. Instructions can be found [here](https://nipoppy.readthedocs.io/en/latest/installation.html) - [Apptainer](https://apptainer.org/) -## Step 1: Initializing the Nipoppy dataset +### Step 1: Initializing the Nipoppy dataset Run the following command to create a Nipoppy dataset and populate it with the BIDS data: @@ -112,7 +112,7 @@ For now, the dataset only has BIDS data. ![](/images/nipoppy-1.png) -## Step 2: Modifying the global configuration file +### Step 2: Modifying the global configuration file The `nipoppy init` command created the configuration file at `nipoppy_example/global_config.json`. This file needs to be updated with information specific to the dataset and your computing environment. By default, the beginning of the global config file looks like this: @@ -164,7 +164,7 @@ There are still paths that need to be filled in for the `SUBSTITUTIONS` field: - ``: a central directory where container images are stored (i.e. if sharing between multiple datasets/projects). This can also be deleted, in which case the `nipoppy_example/containers` directory will be used. - ``: either an existing directory used by TemplateFlow, or an empty directory where templates will be downloaded. -## Step 3: Download the MRIQC Apptainer image +### Step 3: Download the MRIQC Apptainer image Before running the pipeline, download the MRIQC container in the appropriate directory (either `nipoppy_example/containers` or the custom path specified in the global config file) using Apptainer. @@ -172,7 +172,7 @@ Before running the pipeline, download the MRIQC container in the appropriate dir apptainer build /mriqc_23.1.0.sif docker://nipreps/mriqc:23.1.0 ``` -## Step 4: Running MRIQC on a single participant and session +### Step 4: Running MRIQC on a single participant and session Use `nipoppy run` to run MRIQC on a single participant and session. This will take around 15 minutes. @@ -198,7 +198,7 @@ nipoppy_example/derivatives/mriqc/23.1.0/output/ Log files can be found in `nipoppy_example/logs/run/mriqc-23.1.0`. -## Step 5: Tracking pipeline processing status +### Step 5: Tracking pipeline processing status Run `nipoppy track` to determine the MRIQC processing status for each subject and session: @@ -240,7 +240,7 @@ The imaging bagel file can also be uploaded to the [Neurobagel digest dashboard] Finally, this file can be used directly as input to the [Neurobagel CLI](https://neurobagel.org/user_guide/cli/) when generating participant-level metadata about processing pipeline results. -## Step 6 (optional): Running MRIQC on the rest of the dataset +### Step 6 (optional): Running MRIQC on the rest of the dataset Use `nipoppy run` without the participant and session flags to process the rest of the dataset (in a loop). This will skip the participant-session that has previously been run successfully. @@ -248,6 +248,6 @@ Use `nipoppy run` without the participant and session flags to process the rest nipoppy run --pipeline mriqc --pipeline-version 23.1.0 nipoppy_example ``` -# Conclusion +## Conclusion In this tutorial, we ran a neuroimaging pipeline (MRIQC) on a small BIDS dataset, showcasing how Nipoppy keeps track of which participants and sessions are available, and which ones have been processed already. Nipoppy offers a streamlined interface for configuring, running, and tracking virtually any pipeline (including custom ones). Beyond processing pipelines, Nipoppy can also be used to run pipelines for BIDS conversion and for extracting imaging-derived phenotypes (IDPs) from processing pipeline results. diff --git a/content/resources/tutorials/repronim-containers.md b/content/resources/tutorials/repronim-containers.md index 4751385..19581f4 100644 --- a/content/resources/tutorials/repronim-containers.md +++ b/content/resources/tutorials/repronim-containers.md @@ -33,13 +33,13 @@ weight: 5 - Singularity - DataLad -# Challenge +## Challenge Using version control and automation to execute procedures can produce re-executable and provenance-rich results, but the task can appear daunting. Following best-practices for file layouts (Datalad + YODA Principles) provide clear connections (via subdatasets) between the source data and the derivative data that is produced. Additionally, utilizing `datalad run` with `repronim-containers` preserves the provenance of exactly what software versions were used and how, leaving a detailed trail for future work. -# Exercise +## Exercise Let's assume that our goal is to do Quality Control of an MRI dataset (which is available as DataLad dataset ds000003). We will create a new @@ -56,7 +56,7 @@ materials would be *reachable* within that dataset. Note: This exercise is based on the [ReproNim/containers README](https://github.com/ReproNim/containers/), which should be referenced for more information. -## Before you start +### Before you start Required knowledge: @@ -71,9 +71,9 @@ your usecase: - [YODA Organigram](https://github.com/myyoda/poster/blob/master/ohbm2018.pdf) - [Singularity/Apptainer](https://apptainer.org/) -# Step by step guide +## Step by step guide -#### Step 1: Installing the Necessary Tools +### Step 1: Installing the Necessary Tools The following tools should be installed: @@ -86,7 +86,7 @@ Additionally, the `datalad-container` extension should also be installed. pip install datalad-container ``` -#### Step 2: Start a Datalad dataset +### Step 2: Start a Datalad dataset Following YODA, our dataset for the results is **the** dataset that will contain everything needed to produce those results. @@ -96,7 +96,7 @@ cd ~/my-experiments datalad create -d ds000003-qc -c text2git cd ds000003-qc ``` -#### Step 3: Install source data +### Step 3: Install source data Next we install our source data as a subdataset. @@ -104,7 +104,7 @@ Next we install our source data as a subdataset. datalad install -d . -s https://github.com/ReproNim/ds000003-demo sourcedata ``` -#### Step 4: Install ReproNim/containers +### Step 4: Install ReproNim/containers Next we install the `ReproNim/containers` collection. @@ -120,7 +120,7 @@ Now let's take a look at what we have. |--/code/containers # repronim/containers, this is where our non-custom code lives ``` -#### Step 4: Freezing Container Image Versions +### Step 4: Freezing Container Image Versions `freeze_versions` is an optional step that will record and "freeze" the version of the container used. Even if the `///repronim/containers` dataset is @@ -157,7 +157,7 @@ specified in the `.gitmodules`. By freezing into the top-level dataset instead, authors do not need to host a modified version of `///reporonim/containers`. -#### Step 5: Running the Containers +### Step 5: Running the Containers When we run the bids-mriqc container, it will need a working directory for intermediate files. These are not helpful to commit, so we will @@ -215,7 +215,7 @@ singularity container. You can even now [datalad uninstall] sourcedata and even sub-datasets to save space - they will be retrievable at those exact versions later on if you need to extend or redo your analysis. -#### Notes: +### Notes - aforementioned example requires DataLad >= 0.11.5 and datalad-containers >= 0.4.0; - for more eleborate example with use of [reproman] to parallelize execution on From eb0ebe96edda2d2c81dc7f4c23e156647555f16d Mon Sep 17 00:00:00 2001 From: Christian Haselgrove Date: Thu, 26 Dec 2024 15:13:54 -0500 Subject: [PATCH 6/6] Fix spelling errors --- content/about/_index.md | 2 +- content/about/in-practice.md | 2 +- content/about/publications.md | 2 +- content/about/repronim-approach.md | 4 +-- content/about/webinars.md | 2 +- content/resources/getting-started/_index.md | 32 +++++++++---------- content/resources/tools/reproin/_index.md | 2 +- .../tutorials/data-management-and-sharing.md | 8 ++--- content/resources/tutorials/dicom-to-bids.md | 2 +- content/resources/tutorials/git.md | 4 +-- .../tutorials/repronim-containers.md | 4 +-- 11 files changed, 32 insertions(+), 32 deletions(-) diff --git a/content/about/_index.md b/content/about/_index.md index 81187a3..51f9801 100644 --- a/content/about/_index.md +++ b/content/about/_index.md @@ -5,7 +5,7 @@ type: docs ReproNim is a national multi-site technology and research development Center for reproducible neuroimaging computation, funded by a P41 award from the National Institute of Biomedical Imaging and Bioengineering. -Collectively, our project and core teams are based across six sites (UMassChan Medical School, MIT, Dartmouth College, McGill, UCSD, and UCI) in North America. +Collectively, our project and core teams are based across six sites (UMass Chan Medical School, MIT, Dartmouth College, McGill, UCSD, and UCI) in North America. ## What we do diff --git a/content/about/in-practice.md b/content/about/in-practice.md index 7745fc8..a5ebde9 100644 --- a/content/about/in-practice.md +++ b/content/about/in-practice.md @@ -4,7 +4,7 @@ type: docs weight: 80 --- -## ReproNim’s principles of reproducible neuroimaging +## ReproNim's principles of reproducible neuroimaging